497 件 ・ 関連度順
概要と使いどころ
Map a complex topic by placing factors on hexagonal tiles where adjacency signals a claimed relationship. Use when students need to surface hidden connections in a system before analysis or action.
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Use when looking up journal impact factors (JCR IF), checking a journal's impact factor by name, comparing IF across journals, or answering questions about "影响因子" / "impact factor" / "IF". Triggers on "impact factor", "journal IF", "影响因子", "JCR", "IF score", "journal rank", "which journal has higher IF", "what is the IF of". PROACTIVELY USE when user mentions journal prestige, publication venue quality, or manuscript submission target evaluation.
日本語の概要は準備中です。原文の説明を表示しています。
It's 11pm before your launch. Your PH page is ready but you have no idea what actually moves the ranking algorithm. Upvotes? Comments? Time-of-day? This is the hour-by-hour operations manual from someone who's coached 30+ #1 finishes. What's inside: • Ranking algorithm deep-dive (it's not just upvotes — weighted factors decoded) • Launch day minute-by-minute checklist (PST timing, first 3 hours are critical) • Voter mobilization strategies (ethical, anti-spam-safe approaches) • Asset preparation templates (gallery, tagline ≤60 chars, maker comments) • Post-launch 72h momentum plan (convert PH traffic → retained users) Built from: 30+ PH #1 daily wins, 10+ weekly top 3, 2 monthly top 5. 3,000-5,000+ upvotes per launch. Triggers: "Product Hunt" | "PH launch" | "Product Hunt #1" | "PH ranking" | "Product Hunt strategy" | "PH upvotes" | "launch day" | "PH打榜" | "Product Hunt攻略" | "PH ranking algorithm"
日本語の概要は準備中です。原文の説明を表示しています。
Reviews axum web framework code for routing patterns, extractor usage, middleware, state management, and error handling. Use when reviewing Rust code that uses axum, tower, or hyper for HTTP services. Covers axum 0.7+ patterns including State, Path, Query, Json extractors.
日本語の概要は準備中です。原文の説明を表示しています。
Confirms or rejects findings from review-llm-artifacts before deletes or risky refactors. Loads review-verification-protocol-style checks per finding. Use after a review run, when the user wants to reduce false positives, before fix-llm-artifacts on dead code, or when validating a full-project scan.
日本語の概要は準備中です。原文の説明を表示しています。
Decompose and explain healthcare cost growth into contributing factors including utilization changes, unit price shifts, case mix evolution, and population changes. Use when analyzing PMPM cost trends, explaining cost variance to leadership, preparing actuarial summaries, or identifying cost reduction opportunities.
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Access 165+ AI-powered skills for chiropractors and chiropractic office staff. Use when: user asks about patient intake, SOAP notes, insurance billing, HIPAA compliance, appointment scheduling, clinical documentation, practice marketing, or any chiropractic office administration task.
日本語の概要は準備中です。原文の説明を表示しています。
Autonomous orchestrator that scans, refactors, and eradicates technical debt at the end of the development lifecycle to achieve a Zero Debt codebase / Orkestrator otonom untuk menghapus utang teknis sebelum rilis.
日本語の概要は準備中です。原文の説明を表示しています。
Foundational concepts in nutrition science — macronutrients, micronutrients, energy balance, Atwater factors, reference intakes (DRI/RDA/AI/UL), food composition tables, and the methods of human nutrition research. Grounds the rest of the department in a shared vocabulary and in the measurement limits of the field, including controlled-trial history, observational-study limits, and the biochemical basis for calorie accounting. Use when a question asks what something "is" nutritionally, how energy and nutrients are measured, or what the reference numbers mean.
日本語の概要は準備中です。原文の説明を表示しています。
Atlas of Agent Client Protocol (ACP) clients, agents, and OCapN/CapTP-grounded actors. Use when picking a coding agent surface, threading ACP over CapTP, locating the local Zoad implementation, or navigating the Hermes-as-OCapN-actor family.
日本語の概要は準備中です。原文の説明を表示しています。
Review pytest test effectiveness against authoritative contracts, relevant faults, behavior-preserving refactors, realistic boundaries, isolation, and diagnostics. Use for test-quality audits, coverage-gap reviews, regression-test changes, or mocking reviews; preserve existing project gates rather than inventing coverage targets.
日本語の概要は準備中です。原文の説明を表示しています。
Review existing tests against the software and contracts they actually exercise. Use to assess whether a test has justified unique protection, detects consequential faults, tolerates valid refactors, earns its lifecycle cost, or deserves strengthening, replacement, consolidation, or removal consideration. Reviews are read-only and evidence-backed, including TDD and regression tests.
日本語の概要は準備中です。原文の説明を表示しています。
Thread-safe data persistence in Swift using actors — in-memory cache with file-backed storage, eliminating data races by design.
日本語の概要は準備中です。原文の説明を表示しています。
Write unit and integration tests for Akka.NET actors using modern Akka.Hosting.TestKit patterns. Covers dependency injection, TestProbes, persistence testing, and actor interaction verification. Includes guidance on when to use traditional TestKit.
日本語の概要は準備中です。原文の説明を表示しています。
Multi-agent adversarial code review. Spawns three reviewer subagents (Auditor, Adversary, Pragmatist) on a single model, runs a cross-examination round, then deterministically synthesizes a single ranked report. Use this for non-trivial PRs, security-sensitive changes, refactors that touch many files, or any change where one perspective has obvious blind spots. Trigger phrases include "adverse review", "adversarial review", "multi-perspective review", "review my changes from multiple angles", "panel review". NOT suitable for trivial diffs (typos, dependency bumps, formatting).
日本語の概要は準備中です。原文の説明を表示しています。
60-Second Token Snapshot — fast assessment with signal, key factors, and price levels without launching subagents
日本語の概要は準備中です。原文の説明を表示しています。
Deploys custom Apify Actors via the Apify API v2 for large-scale web crawling using CrawleeJS. Leverages Apify dataset storage, RequestQueue, and proxy configuration for distributed scraping at scale.
日本語の概要は準備中です。原文の説明を表示しています。
Builds Apify Actors for scalable cloud scraping with automatic proxy management and storage. Uses the Apify SDK (Actor, Dataset, KeyValueStore, RequestQueue) and Crawlee library for robust crawling.
日本語の概要は準備中です。原文の説明を表示しています。
Executes Apify cloud actors for structured web scraping with automatic dataset export to S3. Supports actor input schema validation and webhook-based run completion notifications.
日本語の概要は準備中です。原文の説明を表示しています。
Deploys intelligent web scraping actors on the Apify platform using the Apify SDK with RequestQueue and Dataset APIs. Handles dynamic content via Apify CheerioCrawler and PlaywrightCrawler with automatic scaling.
日本語の概要は準備中です。原文の説明を表示しています。
Apify SDK is the official JavaScript SDK for building Actors, crawlers, and data extraction workflows on Apify. It gives agents a structured way to run scraping jobs, store outputs, manage inputs, and combine crawler logic with browser automation when needed.
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Orchestrates Apify actors for large-scale web scraping via the Apify Client SDK. Manages actor runs, dataset exports, and proxy configuration through the Apify API v2.
日本語の概要は準備中です。原文の説明を表示しています。
Use jscodeshift when an agent needs AST-based JavaScript or TypeScript codemods for bulk migrations, API rewrites, and large refactors with reviewable diffs.
日本語の概要は準備中です。原文の説明を表示しています。
Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.
日本語の概要は準備中です。原文の説明を表示しています。